Cross-Cloud Analytics: Breaking Down Silos with BigLake

Organizations today face a critical challenge when their data lives across multiple cloud providers. Cross-cloud analytics becomes essential when your customer transactions sit in AWS, your operational logs reside in Azure, and your analytics platform runs on Google Cloud. The traditional approaches to this problem force you into an uncomfortable trade-off between duplicating data everywhere or building complex point-to-point integrations that become maintenance nightmares.

BigLake from Google Cloud addresses this challenge by creating a unified analytics layer that spans multiple clouds without requiring you to copy data or sacrifice governance controls. Understanding how this works and when it makes sense compared to traditional approaches will help anyone building modern data platforms.

The Traditional Approach: Data Replication

The conventional method for enabling analytics across multiple clouds involves copying data from each source into a centralized data warehouse. A logistics company might extract shipment tracking data from AWS S3, customer feedback from Azure Blob Storage, and route optimization data from Google Cloud Storage, then load everything into BigQuery.

This approach offers clear advantages. Once data lives in a single location, query performance becomes predictable and fast. You control the data format, can optimize storage layouts for your access patterns, and apply consistent security policies through one system. For many organizations, this pattern works well enough.

Here's what a typical replication workflow looks like:

from google.cloud import storage
import boto3

# Extract from AWS S3
s3_client = boto3.client('s3')
response = s3_client.get_object(Bucket='shipments', Key='daily_tracking.parquet')
data = response['Body'].read()

# Load to GCS, then BigQuery
gcs_client = storage.Client()
bucket = gcs_client.bucket('unified-analytics')
blob = bucket.blob('shipments/daily_tracking.parquet')
blob.upload_from_string(data)

# Then run BigQuery load job

When Replication Makes Sense

Data replication works well when your source systems are relatively static, update frequencies are predictable, and you need maximum query performance. A retail bank analyzing transaction patterns from the previous month doesn't need real-time access to the source systems. Copying that data into BigQuery and running optimized queries makes perfect sense.

The data becomes fully yours to optimize. You can partition tables by transaction date, cluster by customer region, and build materialized views without worrying about the source system's capabilities or costs.

Drawbacks of Data Replication

The problems emerge as your data landscape grows. That logistics company now has 50TB of shipment data in AWS that updates continuously throughout the day. Replicating everything means:

Storage costs multiply. You're paying AWS for the original storage and Google Cloud for the copy. With data growing constantly, these costs compound month after month.

Freshness becomes a battle. Your analytics are always behind reality by however long your replication pipeline takes. If the pipeline runs hourly, your dashboards show data that's at least an hour old. Speed up the pipeline and you increase costs and complexity.

Data governance fragments. When a customer requests deletion under privacy regulations, you need to track down and remove their data from the source system AND all copies. Miss one replica and you've got a compliance problem. The same issue applies to access controls. When an employee changes roles, you need to update permissions everywhere.

Consider this scenario: A pharmaceutical research company has genomic sequencing data in AWS (managed by their research team), clinical trial results in Azure (from an acquired company), and patient outcome data in GCP. Replicating everything would mean:

-- Each query combines data that was copied at different times
SELECT 
  aws_genomics.patient_id,
  aws_genomics.sequence_data,  -- Replicated at 2 AM
  azure_trials.trial_outcome,  -- Replicated at 3 AM
  gcp_outcomes.patient_status  -- Real-time
FROM replicated_aws_genomics AS aws_genomics
JOIN replicated_azure_trials AS azure_trials
  ON aws_genomics.patient_id = azure_trials.patient_id
JOIN patient_outcomes AS gcp_outcomes
  ON aws_genomics.patient_id = gcp_outcomes.patient_id
WHERE trial_date > '2024-01-01';

This query joins data with inconsistent timestamps. The genomics data is hours old, the trial data is slightly fresher, and only the outcomes are current. This temporal misalignment can lead to incorrect conclusions in time-sensitive research.

The Alternative: Federated Query Access

Federated queries flip the model. Instead of copying data, you query it directly where it lives. BigQuery has supported this for years through external tables and connections. You point BigQuery at data in Cloud Storage, AWS S3, or Azure Blob Storage, and it queries that data in place.

This approach eliminates data duplication entirely. Your storage costs stay confined to the original location. Data freshness is immediate because you're always reading the current state. Governance simplifies because there's only one copy to secure and manage.

A basic federated query looks like this:

-- Create external table pointing to AWS S3
CREATE EXTERNAL TABLE `project.dataset.shipments_external`
OPTIONS (
  format = 'PARQUET',
  uris = ['s3://shipments-bucket/daily_tracking/*.parquet']
);

-- Query directly without copying
SELECT 
  shipment_id,
  origin,
  destination,
  delivery_time
FROM `project.dataset.shipments_external`
WHERE delivery_date = CURRENT_DATE();

However, traditional federated queries come with significant trade-offs. Query performance suffers because data hasn't been optimized for analytics workloads. Security becomes complex because you're managing permissions across multiple systems. Caching is limited or nonexistent. These limitations mean federated queries work well for occasional exploratory analysis but struggle when you need production-grade performance and governance.

How BigLake Addresses Cross-Cloud Analytics

BigLake fundamentally changes the calculus for cross-cloud analytics by bringing BigQuery's enterprise features to federated data access. Rather than choosing between replication with good performance or federation with poor governance, BigLake provides a middle path that delivers both.

The architecture positions BigLake as a metadata and security layer that sits between your query engines and your data sources. When you query data through BigLake, you're reading files from S3 or Azure Blob Storage through a system that understands table schemas, applies row and column level security, uses intelligent caching, and maintains consistent permissions across clouds.

Here's what changes with BigLake tables compared to traditional external tables:

-- Create a BigLake table instead of a standard external table
CREATE EXTERNAL TABLE `project.dataset.shipments_biglake`
WITH CONNECTION `project.region.aws_connection`
OPTIONS (
  format = 'PARQUET',
  uris = ['s3://shipments-bucket/daily_tracking/*.parquet'],
  max_staleness = INTERVAL 4 HOUR
);

-- Apply column-level security
CREATE OR REPLACE ROW ACCESS POLICY shipments_regional_filter
ON `project.dataset.shipments_biglake`
GRANT TO ('group:regional-managers@company.com')
FILTER USING (region = SESSION_USER().region);

-- Same query syntax, but now with caching and security
SELECT 
  shipment_id,
  origin,
  destination,
  delivery_time
FROM `project.dataset.shipments_biglake`
WHERE delivery_date = CURRENT_DATE();

The key architectural difference is that BigLake tables support fine-grained access controls that work consistently whether data lives in GCS, AWS S3, or Azure Blob Storage. You define security policies once in BigQuery, and they apply everywhere. This addresses one of the biggest pain points in multi-cloud analytics where permissions management becomes exponentially complex as you add data sources.

BigLake also enables query result caching across external data sources. When multiple users run similar queries against your AWS data, BigLake can serve results from cache rather than repeatedly scanning the source files. This dramatically improves performance for common query patterns while still maintaining data freshness within the staleness threshold you define.

Another significant capability is that BigLake tables work with multiple query engines beyond BigQuery. You can query the same BigLake table using Apache Spark through Dataproc, Trino, or even Vertex AI for machine learning workloads. This means your data scientists can use their preferred tools while still benefiting from unified governance.

BigQuery Omni and Multi-Cloud Integration

BigQuery Omni extends BigLake's capabilities by allowing you to run BigQuery queries directly in AWS and Azure regions without moving data to GCP. This becomes valuable when data gravity or regulatory requirements mean data cannot leave a specific cloud provider or geographic region.

A healthcare technology company might use BigLake and BigQuery Omni together to analyze patient data that legally must remain in specific AWS regions while combining it with operational data in GCP. The queries run where the data lives, but you manage everything through the BigQuery interface.

Real-World Scenario: Global Streaming Service

Consider a video streaming platform that serves 80 million subscribers across six continents. Their architecture evolved through acquisitions and regional expansion, resulting in data spread across all three major clouds. Viewing history and playback quality metrics sit in AWS DynamoDB and S3 for North America and Europe. Content metadata and encoding jobs live in Azure Blob Storage from an acquired European competitor's infrastructure. User profiles and recommendation models run in Google Cloud, their original platform.

The analytics team needs to answer questions like "How does network quality affect viewing completion rates for different content types across regions?" This requires joining terabytes of viewing data from AWS with content metadata from Azure and user segments from GCP.

Replication Approach Costs

If they replicated everything to BigQuery, the AWS viewing data of 120TB monthly costs roughly $2,760/month in S3 plus $2,760/month in GCS replication, totaling $5,520. Azure content metadata of 15TB monthly adds similar duplication costs of $690. Data transfer egress from AWS and Azure runs approximately $10,800/month for 120TB. Pipeline infrastructure and maintenance adds additional compute and engineering time.

Total monthly cost exceeds $17,000 just for data movement and duplication, not counting the query compute costs or the engineering effort to build and maintain replication pipelines.

BigLake Implementation

With BigLake, they create external tables pointing to each data source:

-- AWS viewing data
CREATE EXTERNAL TABLE `streaming.analytics.viewing_history`
WITH CONNECTION `us.aws_connection`
OPTIONS (
  format = 'PARQUET',
  uris = ['s3://viewing-history/year=*/month=*/day=*/*.parquet'],
  max_staleness = INTERVAL 1 HOUR,
  metadata_cache_mode = 'AUTOMATIC'
);

-- Azure content metadata
CREATE EXTERNAL TABLE `streaming.analytics.content_catalog`
WITH CONNECTION `europe-west1.azure_connection`
OPTIONS (
  format = 'PARQUET',
  uris = ['azure://content-metadata/*.parquet'],
  max_staleness = INTERVAL 6 HOUR
);

-- Analysis query joins across clouds
SELECT 
  c.content_type,
  c.duration_minutes,
  u.subscription_tier,
  AVG(v.completion_percentage) as avg_completion,
  AVG(v.quality_score) as avg_quality,
  COUNT(*) as total_views
FROM `streaming.analytics.viewing_history` v
JOIN `streaming.analytics.content_catalog` c
  ON v.content_id = c.content_id
JOIN `streaming.analytics.user_profiles` u
  ON v.user_id = u.user_id
WHERE v.view_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)
  AND v.quality_score IS NOT NULL
GROUP BY c.content_type, c.duration_minutes, u.subscription_tier
ORDER BY avg_completion DESC;

This query runs across three clouds without copying any data. BigLake's caching layer means frequently accessed content metadata and user profiles get cached, so subsequent queries run faster. The max_staleness setting ensures they're never analyzing viewing data more than an hour old.

The cost profile changes dramatically. There are no storage duplication costs. Data transfer costs only apply to query results, not full datasets. BigQuery compute charges apply only for actual queries run. There's no pipeline infrastructure to maintain.

For their workload pattern, monthly costs dropped to roughly $4,500, a 75% reduction. Query freshness improved from "data replicated overnight" to "current within one hour."

Security Implementation

The streaming service implements column-level security through BigLake to protect personally identifiable information:

-- Create policy tags for data classification
CREATE TAXONOMY `streaming.data_classification`;

CREATE POLICY TAG `streaming.data_classification.pii`;

-- Apply to specific columns in BigLake tables
ALTER TABLE `streaming.analytics.viewing_history`
ALTER COLUMN user_email 
SET OPTIONS (policy_tags=['projects/project-id/locations/us/taxonomies/12345/policyTags/67890']);

-- Grant access only to authorized roles
GRANT `roles/datacatalog.categoryFineGrainedReader`
ON POLICY TAG `streaming.data_classification.pii`
TO 'group:privacy-team@company.com';

This security configuration applies consistently whether the underlying data lives in AWS, Azure, or GCP. The privacy team doesn't need to manage three separate permission systems.

Decision Framework for Cross-Cloud Analytics

Choosing between data replication and BigLake depends on your specific requirements and constraints. Here's how to evaluate your situation:

Factor	Data Replication	BigLake
Data Freshness Requirements	Acceptable if hourly or daily updates suffice	Better when you need near real-time access
Query Performance Priority	Superior for heavily optimized workloads with clustering and partitioning	Good with caching, excellent for ad-hoc analysis
Storage Budget	Higher costs due to duplication	Lower costs, pay only at source
Governance Complexity	Must manage permissions and compliance across copies	Unified governance through BigQuery
Data Volume	Expensive at scale due to transfer and duplication	Cost-effective as volume grows
Query Patterns	Optimal for repeated queries on same datasets	Better for exploratory and diverse query patterns
Regulatory Constraints	Complex when data cannot be moved	Simpler, data stays in original location

Some scenarios clearly favor one approach. If you're running a high-frequency trading platform where millisecond query latency matters and your data naturally centralizes in GCP, replication makes sense. The performance benefits outweigh duplication costs.

Conversely, a pharmaceutical company conducting clinical research across acquired companies with data in multiple clouds benefits enormously from BigLake. Data can't easily move due to regulations, governance complexity is high, and query patterns are exploratory rather than repetitive.

Hybrid Approaches

Many organizations end up using both patterns strategically. They might replicate high-value, frequently queried datasets into BigQuery for optimal performance while using BigLake for long-tail data accessed occasionally. A media company could replicate the last 90 days of viewing data for real-time dashboards while keeping historical archives accessible through BigLake.

Cross-cloud analytics isn't a binary choice. Google Cloud provides tools for both patterns, and thoughtful architects combine them based on specific needs for each dataset.

Making the Right Choice

Cross-cloud analytics forces a fundamental trade-off between data accessibility, performance, cost, and governance complexity. Traditional data replication offers excellent query performance but multiplies costs and creates governance headaches. Basic federated queries save on duplication but sacrifice performance and security capabilities.

BigLake from Google Cloud reframes this trade-off by extending BigQuery's enterprise features to data wherever it lives. You gain unified governance, intelligent caching, and consistent security without copying data across clouds. This becomes particularly valuable as data volumes grow, regulatory requirements tighten, and organizations need to break down data silos across multi-cloud environments.

The right approach depends on your specific context. Evaluate your data freshness requirements, query performance needs, storage budgets, and governance complexity. Sometimes replication remains the best choice. Sometimes BigLake transforms what's possible. Often, a hybrid strategy optimizes for both.

Understanding these trade-offs matters whether you're architecting production systems or preparing for Google Cloud certification exams. The Professional Data Engineer exam specifically tests your ability to design multi-cloud data solutions and choose appropriate tools based on requirements. Readers looking for comprehensive exam preparation can check out the Professional Data Engineer course to dive deeper into BigLake, BigQuery, and the full range of GCP data services.

Thoughtful engineering means recognizing that breaking down data silos is a strategic decision that affects costs, compliance, and analytical capabilities across your entire organization. Choose the approach that aligns with your constraints, and be ready to evolve as your needs change.